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From Learning to CAD to CADing to Learn: Teaching the Command, Strategic, and Epistemic Dimensions of CAD Software

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

EDGD: CAD, CAM, and AI

Tagged Division

Engineering Design Graphics

Page Count

19

Permanent URL

https://peer.asee.org/30543

Download Count

33

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Paper Authors

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Dean Nieusma Rensselaer Polytechnic Institute Orcid 16x16 orcid.org/0000-0003-2711-3315

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Dean Nieusma is Associate Dean for Curricular Transitions, Associate Professor in Science and Technology Studies, and Director of the Programs in Design and Innovation at Rensselaer.

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James W. Malazita Rensselaer Polytechnic Institute

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James Malazita is an Assistant Professor in Science & Technology Studies and Games & Simulation Arts & Sciences at Rensselaer Polytechnic Institute.

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Lydia Rebekka Krauss Rensselaer Polytechnic Institute

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Andrea M. Ukleja

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2016 graduate of Rensselaer Polytechnic Institute with a B.S. in Design, Innovation and Society & Mechanical Engineering. Currently employed by Hasbro, Inc. as an Associate Reliability Engineer.

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Timothy Andrews Rensselaer Polytechnic Institute

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Timothy Andrews is an undergraduate student at Rensselaer studying Mechanical Engineering and Programs in Design and Innovation.

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Abstract

This paper describe the development and early-stage assessment of a novel approach to interdisciplinary Computer Aided Design (CAD) instruction. Traditional CAD instruction takes a command-centered approach, demanding that students successfully replicate sample engineering models, assemblies, and associated drafts. This educational approach is effective in helping students gain sufficient declarative knowledge—that is, knowledge about the relationships between user inputs and corresponding software responses in order to replicate pre-specified component parts and assemblies. Drawing on Ivan Chester’s framework of CAD knowledge domains, this method succeeds in conveying declarative and procedural knowledge but falls short in teaching strategic knowledge required to effectively employ CAD as a design tool. According to Chester, strategic knowledge involves higher-order design decision making and is critical because it is transferable across CAD software packages.

Our research, funded by an internal pedagogical innovation grant, takes Chester's approach to the next level. We hypothesize that a more engaging learning process—emphasizing both strategic CAD knowledge and what we call "epistemic CAD knowledge"—can convey these knowledges alongside declarative and procedural expertise. To test this hypothesis, a cross-disciplinary team of instructors and undergraduate students—representing engineering, design, social sciences, and humanities—conducted a series of CAD instructional workshops employing a variety of educational exercises and assessing student knowledge acquisition in declarative, procedural, strategic, and epistemic domains. These exercises intersected the teaching of CAD procedures and strategies with collaborative, open-ended design prompts. We assessed students’ CAD learning in conjunction with their responses to the design prompts.

Our findings indicate students were, in fact, more engaged by the strategic and epistemic emphasis of our educational experiments, and yet consistent attention to declarative and procedural knowledge was still required throughout all levels of CAD instruction. These findings suggest that while command-centric learning should remain integral to CAD instruction, emphasizing higher-order design thinking in the context of CAD instruction can change how students relate to the tool, and through this can be effective in transforming student understanding of how CAD inflects engineering design output.

Nieusma, D., & Malazita, J. W., & Krauss, L. R., & Ukleja, A. M., & Andrews, T. (2018, June), From Learning to CAD to CADing to Learn: Teaching the Command, Strategic, and Epistemic Dimensions of CAD Software Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/30543

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